Last updated: 2020-11-10

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Knit directory: T47D_ZR75_DHT_StrippedSerum_RNASeq/analysis/

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File Version Author Date Message
Rmd 7d3cf7f Steve Ped 2020-11-10 Added count merging
html ef81e6a Steve Ped 2020-11-10 Build site.
html 24c2e2f Steve Ped 2020-11-10 Build site.
Rmd 13c3089 Steve Ped 2020-11-10 Recompiled pre-alignment qc
html 6985f16 Steve Ped 2020-11-09 Build site.
Rmd 30ecf4f Steve Ped 2020-11-09 Corrected errors
html c8e1455 Steve Ped 2020-11-09 Build site.
Rmd fda3bb2 Steve Ped 2020-11-09 Rewrote qc_raw
html ea635ab Steve Ped 2020-11-09 Build site.
Rmd 8d91bfc Steve Ped 2020-11-09 Rewrote qc_raw
html 3502d11 Steve Ped 2020-11-09 Build site.
Rmd 9d03800 Steve Ped 2020-11-09 Added tabsets for both runs
Rmd 6f7af53 Steve Pederson 2020-11-06 Initial Commit 
library(ngsReports)
library(tidyverse)
library(yaml)
library(scales)
library(pander)
library(glue)
library(plotly)
library(msa)
panderOptions("table.split.table", Inf)
panderOptions("big.mark", ",")
theme_set(theme_bw())
config <- here::here("config/config.yml") %>%
  read_yaml()
suffix <- paste0(config$tags$tag, config$ext)
sp <- config$ref$species %>%
  str_replace("(^[a-z])[a-z]*_([a-z]+)", "\\1\\2") %>%
  str_to_title()
runRegEx <- paste(str_split(config$runs, " ")[[1]], collapse = "|")
samples <- config$samples %>%
  here::here() %>%
  read_tsv() %>%
  mutate(
    R1 = paste0(sample, config$tags$r1, suffix),
    R2 = paste0(sample, config$tags$r2, suffix),
  ) %>%
  pivot_longer(
    cols = c("R1", "R2"),
    names_to = "Reads",
    values_to = "Filename"
  ) %>%
  mutate_if(
    function(x){length(unique(x)) < length(x)},
    as.factor
  )
config$analysis <- config$analysis %>%
  lapply(intersect, y = colnames(samples)) %>%
  .[vapply(., length, integer(1)) > 0]
if (length(config$analysis)) {
  samples <- samples %>%
    unite(
      col = group, 
      any_of(as.character(unlist(config$analysis))), 
      sep = "_", remove = FALSE
    )
} else {
  samples$group <- samples$Filename
}
group_cols <- hcl.colors(
  n = length(unique(samples$group)), 
  palette = "Zissou 1"
  ) %>%
  setNames(unique(samples$group))
fh <- round(6 + nrow(samples) / 7, 0)

Quality Assessment on Raw Data

rawFqc <- here::here("data/raw/FastQC") %>%
  list.files(pattern = "fastqc.zip", full.names = TRUE, recursive = TRUE) %>%
  FastqcDataList() %>%
  .[fqName(.) %in% samples$Filename]
for (i in seq_along(rawFqc)){
  run <- str_extract(path(rawFqc[[i]]), runRegEx)
  rawFqc[[i]]@Summary$Filename <- paste(run, rawFqc[[i]]@Summary$Filename, sep = "/")
}

FastQC Summary

plotSummary(rawFqc)
*Overall summary of FastQC reports*

Overall summary of FastQC reports

Version Author Date
ea635ab Steve Ped 2020-11-09
3502d11 Steve Ped 2020-11-09

Library Sizes

A total of 64 libraries were contained in this dataset, with read totals ranging between 5,699,001 and 28,623,352 reads.

Across all libraries, reads were between 35 and 101 bases. This does indicate some read trimming had been performed prior to that undertaken here.

plotReadTotals(rawFqc, pattern = suffix, usePlotly = TRUE)

Library Sizes for all supplied fastq files. Any samples run as multiple libraries are shown as the supplied multiple libraries and have not been merged.

Sequence Quality

plotBaseQuals(
  rawFqc,
  pattern = suffix, 
  usePlotly = TRUE,
  dendrogram = TRUE,
  cluster = TRUE
  )

Mean sequencing quality scores at each base position for each library

GC Content

GC content patterns for both R1 and R2 reads were highly reminiscent of those seen when rRNA has not been completely removed.

plotGcContent(rawFqc[grepl("R1", fqName(rawFqc))], plotType = "line", usePlotly = TRUE, gcType = "Trans")

GC content for all R1 libraries 

plotGcContent(rawFqc[grepl("R2", fqName(rawFqc))], plotType = "line", usePlotly = TRUE, gcType = "Trans")

GC content for all R2 libraries

Sequence Content

plotly::ggplotly(
  getModule(rawFqc, module = "Per_base_sequence_content") %>% 
    mutate(Base = fct_inorder(Base)) %>%
    group_by(Base) %>% 
    mutate(
      across(c("A", "C", "G", "T"), function(x){x - mean(x)}) 
    ) %>% 
    pivot_longer(
      cols = c("A", "C", "G", "T"), 
      names_to = "Nuc", 
      values_to = "resid"
    ) %>%
    separate(Filename, into = c("Run", "Filename"), sep = "/") %>%
    left_join(samples) %>%
    unite(Filename, Run, Filename, sep = "/") %>%
    ggplot(
      aes(Base, resid, group = Filename, colour = group)
    ) + 
    geom_line() +
    facet_wrap(~Nuc) + 
    scale_colour_manual(values = group_cols) +
    labs(
      x = "Read Position", y = "Residual", colour = "Group"
    )
)

Base and Position specific residuals for each sample. The mean base content at each position was calculated for each nucleotide, and the sample-specific residuals calculated.

AdapterContent

plotAdapterContent(
  x = rawFqc, 
  pattern = suffix, 
  usePlotly = TRUE,
  dendrogram = TRUE,
  cluster = TRUE
  )

Total Adapter Content for each sample shown by starting position in the read.

Overrepresented Sequences

os <- suppressMessages(getModule(rawFqc, "Over"))
os_fh <- min(20, 6 + nrow(os) / 20)
if (nrow(os)){
  if (length(unique(os$Filename)) > 1){
    suppressMessages(
      plotOverrep(
        x = rawFqc,
        pattern = suffix, 
        usePlotly = TRUE,
        dendrogram = TRUE,
        cluster = TRUE
      )
    )
  }
}

Summary of over-represented sequences across all libraries 

os %>%
  group_by(Sequence, Possible_Source) %>%
  summarise(
    `Found in` = n(),
    Total = sum(Count),
    `Largest Percent` = glue("{round(max(Percentage), 2)}%")
  ) %>%
  ungroup() %>%
  arrange(desc(Total)) %>%
  dplyr::filter(Total > 5e5) %>%
  mutate(Rank = seq_along(Total)) %>%
  dplyr::select(Rank, everything()) %>%
  pander(
    caption = "*Summary of most abundant over-represented sequences within the raw data.*"
  )
Summary of most abundant over-represented sequences within the raw data.
Rank Sequence Possible_Source Found in Total Largest Percent
1 CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG No Hit 32 2,775,012 0.88%
2 CTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTCCTACAAATGGACCTT No Hit 32 1,869,452 0.64%
3 CTCCGTTTCCGACCTGGGCCGGTTCACCCCTCCTTAGGCAACCTGGTGGT No Hit 32 1,667,442 0.53%
4 CTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGA No Hit 32 1,551,495 0.63%
5 CCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATAT No Hit 32 1,505,323 0.53%
6 CCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATT No Hit 32 1,382,213 0.5%
7 CTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC No Hit 30 1,173,525 0.47%
8 CCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTAC No Hit 16 1,082,742 0.56%
9 CTTGGTTATAATTTTTCATCTTTCCCTTGCGGTACTATATCTATTGCGCC No Hit 30 1,075,631 0.51%
10 CTCTCTACAAGGTTTTTTCCTAGTGTCCAAAGAGCTGTTCCTCTTTGGAC No Hit 32 1,048,457 0.35%
11 CCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCA No Hit 26 1,042,034 0.53%
12 CCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTG No Hit 32 989,640 0.31%
13 CTTATTTCTCTTGTCCTTTCGTACAGGGAGGAATTTGAAGTAGATAGAAA No Hit 32 925,237 0.35%
14 CTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACT No Hit 32 893,807 0.3%
15 CTGGGCTGTAGTGCGCTATGCCGATCGGGTGTCCGCACTAAGTTCGGCAT No Hit 30 830,226 0.26%
16 CTCTAGAATAGGATTGCGCTGTTATCCCTAGGGTAACTTGTTCCGTTGGT No Hit 31 828,390 0.27%
17 CGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTTCTCTTGTCCTTTCGTA No Hit 32 800,786 0.37%
18 CCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCTTGAGTCCAGGAGTT No Hit 32 797,383 0.25%
19 CTCCGAGGTCGCCCCAACCGAAATTTTTAATGCAGGTTTGGTAGTTTAGG No Hit 32 778,604 0.25%
20 CTGGTTTCGGGGGTCTTAGCTTTGGCTCTCCTTGCAAAGTTATTTCTAGT No Hit 30 777,092 0.29%
21 CTTGAGTCCAGGAGTTCTGGGCTGTAGTGCGCTATGCCGATCGGGTGTCC No Hit 30 768,861 0.23%
22 CGTTGGTCAAGTTATTGGATCAATTGAGTATAGTAGTTCGCTTTGACTGG No Hit 30 740,767 0.22%
23 CCCAAACCCACTCCACCCTACTACCAGACAACCTTAGCCAAACCATTTAC No Hit 16 729,714 0.4%
24 GTTCTGGGCTGTAGTGCGCTATGCCGATCGGGTGTCCGCACTAAGTTCGG No Hit 30 725,496 0.23%
25 CTCAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATC No Hit 25 719,900 0.27%
26 CAGGCTGGAGTGCAGTGGCTATTCACAGGCGCGATCCCACTACTGATCAG No Hit 25 699,697 0.27%
27 CTTTAATCGTTGAACAAACGAACCTTTAATAGCGGCTGCACCATCGGGAT No Hit 31 691,249 0.21%
28 CTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAG No Hit 32 664,586 0.21%
29 CCCTGTTCTTGGGTGGGTGTGGGTATAATGCTAAGTTGAGATGATATCAT No Hit 15 659,116 0.47%
30 CCCTGTTCTTGGGTGGGTGTGGGTATAATACTAAGTTGAGATGATATCAT No Hit 15 641,218 0.4%
31 GTCCAATTGGGTGTGAGGAGTTCAGTTATATGTTTGGGATTTTTTAGGTA No Hit 25 620,097 0.29%
32 CTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCT No Hit 28 617,871 0.23%
33 CTCATTTGGATGTGTCTGGAGTCTTGGAAGCTTGACTACCCTACGTTCTC No Hit 29 610,661 0.24%
34 CCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTT No Hit 29 604,876 0.19%
35 GTATAATACTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTTTGTG No Hit 16 600,508 0.44%
36 CGCTTGAGTCCAGGAGTTCTGGGCTGTAGTGCGCTATGCCGATCGGGTGT No Hit 29 589,770 0.21%
37 CTCAGACCGCGTTCTCTCCCTCTCACTCCCCAATACGGAGAGAAGAACGA No Hit 23 589,407 0.31%
38 GCTACCTTTGCACGGTTAGGGTACCGCGGCCGTTAAACATGTGTCACTGG No Hit 23 564,289 0.21%
39 CACCAGGTTGCCTAAGGAGGGGTGAACCGGCCCAGGTCGGAAACGGAGCA No Hit 26 551,677 0.21%
40 GTGGGTATAATGCTAAGTTGAGATGATATCATTTACGGGGGAAGGCGCTT No Hit 15 547,997 0.37%
41 CTTTCGTACAGGGAGGAATTTGAAGTAGATAGAAACCGACCTGGATTACT No Hit 26 543,299 0.22%
42 CGCGTGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCTGGAGGATCGCT No Hit 27 517,875 0.17%
43 GTAAGATTTGCCGAGTTCCTTTTACTTTTTTTAACCTTTCCTTATGGGCA No Hit 15 501,227 0.33% 
os %>%
  group_by(Sequence, Possible_Source) %>%
  summarise(
    Total = sum(Count), .groups = "drop"
  ) %>%
  arrange(desc(Total)) %>%
  pull("Sequence") %>%
  setNames(paste0("os", seq_along(.))) %>%
  str_subset("TGGTCCCCCGCTCCCGGGAGG") %>%
  as("DNAStringSet") %>%
  msa() %>%
  as("DNAStringSet")
use default substitution matrix
DNAStringSet object of length 9:
    width seq
[1]    66 CCCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATAT----------------
[2]    66 -CCCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATT---------------
[3]    66 --CCTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTG--------------
[4]    66 ---CTCCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGA-------------
[5]    66 -----CCTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG-----------
[6]    66 -----ACTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATG-----------
[7]    66 ------CTTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC----------
[8]    66 ------ATTAGGCAACCTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGC----------
[9]    66 ----------------CTGGTGGTCCCCCGCTCCCGGGAGGTCACCATATTGATGCCGAACTTAGT

A BLAST search of 10 manually selected sequences (which are clearly the same sequence) revealed this to be a perfect match to multiple genomic locations such as RPS29, RN7SL3, NEMF and RN7SL396P. It is clear that this is likely to have some association with rRNA.

os %>%
  group_by(Sequence, Possible_Source) %>%
  summarise(
    Total = sum(Count), .groups = "drop"
  ) %>%
  ungroup() %>%
  arrange(desc(Total)) %>%
  pull("Sequence") %>%
  setNames(paste0("os", seq_along(.))) %>%
  str_subset("TATTTCTCTTGTCCTTTCGTA") %>%
  as("DNAStringSet") %>%
  msa() %>%
  as("DNAStringSet")
use default substitution matrix
DNAStringSet object of length 7:
    width seq
[1]    77 CGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTT...TTCGTA---------------------------
[2]    77 AGGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTT...TTCGTA---------------------------
[3]    77 -GGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTT...TTCGTAC--------------------------
[4]    77 -AGGGGAAGGCGCTTTGTGAAGTAGGCCTTATTT...TTCGTAC--------------------------
[5]    77 ---------------------------CTTATTT...TTCGTACAGGGAGGAATTTGAAGTAGATAGAAA
[6]    77 --------------------------CCTTATTT...TTCGTACAGGGAGGAATTTGAAGTAGATAGAA-
[7]    77 -------------------------GCCTTATTT...TTCGTACAGGGAGGAATTTGAAGTAGATAGA--

A manual search using additional over-represented sequences which did not align to the above consensus, revealed matches to additional SRP RNAs and 7S RNA

As such, rRNA may present some issues for this dataset as these sequences will not be removed by adapter removal. Alignment to a rRNA genome may be of benefit.


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] msa_1.22.0          Biostrings_2.58.0   XVector_0.30.0     
 [4] IRanges_2.24.0      S4Vectors_0.28.0    plotly_4.9.2.1     
 [7] glue_1.4.2          pander_0.6.3        scales_1.1.1       
[10] yaml_2.2.1          forcats_0.5.0       stringr_1.4.0      
[13] dplyr_1.0.2         purrr_0.3.4         readr_1.4.0        
[16] tidyr_1.1.2         tidyverse_1.3.0     ngsReports_1.6.0   
[19] tibble_3.0.4        ggplot2_3.3.2       BiocGenerics_0.36.0
[22] workflowr_1.6.2    

loaded via a namespace (and not attached):
 [1] colorspace_1.4-1            hwriter_1.3.2              
 [3] ellipsis_0.3.1              rprojroot_1.3-2            
 [5] GenomicRanges_1.42.0        ggdendro_0.1.22            
 [7] fs_1.5.0                    rstudioapi_0.11            
 [9] farver_2.0.3                ggrepel_0.8.2              
[11] DT_0.16                     fansi_0.4.1                
[13] lubridate_1.7.9             xml2_1.3.2                 
[15] leaps_3.1                   knitr_1.30                 
[17] jsonlite_1.7.1              Cairo_1.5-12.2             
[19] Rsamtools_2.6.0             broom_0.7.2                
[21] cluster_2.1.0               dbplyr_2.0.0               
[23] png_0.1-7                   compiler_4.0.3             
[25] httr_1.4.2                  backports_1.2.0            
[27] assertthat_0.2.1            Matrix_1.2-18              
[29] lazyeval_0.2.2              cli_2.1.0                  
[31] later_1.1.0.1               htmltools_0.5.0            
[33] tools_4.0.3                 gtable_0.3.0               
[35] GenomeInfoDbData_1.2.4      reshape2_1.4.4             
[37] FactoMineR_2.3              ShortRead_1.48.0           
[39] Rcpp_1.0.5                  Biobase_2.50.0             
[41] cellranger_1.1.0            vctrs_0.3.4                
[43] crosstalk_1.1.0.1           xfun_0.19                  
[45] rvest_0.3.6                 lifecycle_0.2.0            
[47] zlibbioc_1.36.0             MASS_7.3-53                
[49] zoo_1.8-8                   hms_0.5.3                  
[51] promises_1.1.1              MatrixGenerics_1.2.0       
[53] SummarizedExperiment_1.20.0 RColorBrewer_1.1-2         
[55] latticeExtra_0.6-29         stringi_1.5.3              
[57] highr_0.8                   BiocParallel_1.24.0        
[59] GenomeInfoDb_1.26.0         rlang_0.4.8                
[61] pkgconfig_2.0.3             matrixStats_0.57.0         
[63] bitops_1.0-6                evaluate_0.14              
[65] lattice_0.20-41             labeling_0.4.2             
[67] GenomicAlignments_1.26.0    htmlwidgets_1.5.2          
[69] tidyselect_1.1.0            here_0.1                   
[71] plyr_1.8.6                  magrittr_1.5               
[73] R6_2.5.0                    generics_0.1.0             
[75] DelayedArray_0.16.0         DBI_1.1.0                  
[77] pillar_1.4.6                haven_2.3.1                
[79] whisker_0.4                 withr_2.3.0                
[81] scatterplot3d_0.3-41        RCurl_1.98-1.2             
[83] modelr_0.1.8                crayon_1.3.4               
[85] rmarkdown_2.5               jpeg_0.1-8.1               
[87] grid_4.0.3                  readxl_1.3.1               
[89] data.table_1.13.2           git2r_0.27.1               
[91] reprex_0.3.0                digest_0.6.27              
[93] flashClust_1.01-2           httpuv_1.5.4               
[95] munsell_0.5.0               viridisLite_0.3.0